Using an NVIDIA DGX-2 system running accelerated Python libraries, NVIDIA shattered the previous STAC-A3 benchmark result by running 20 million simulations versus the previous record of 3,200 simulations during the prescribed 60-minute test period.

Financial modeling for trading involves a considerable amount of expertise and time. The speed of NVIDIA-accelerated systems enables new design choices for a variety of models.

– How GPU-Accelerated Compute Marks A New Era for Financial Trading technical brief

Fraud Detection

The complexity of fraudulent activity, such as payment theft and money laundering, has evolved in proportionate to advancements in technology. Deep learning (DL) dramatically reduces false positives in transactional fraud.

With the availability of large volumes of customer data, such as raw transactions over time (RNN) and transaction summary vectors (RNN and CNN), firms can train AI neural networks like autoencoders and models to identify irregularities in transactional activity patterns.